When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or “spins”, after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically proven pulse sequences and they also enable the development of new pulse sequences.
In an effort to increase contrast attributable to the relatively small signal levels or weight particular signals attributable to cerebral blood flow (CBF) or another measurable mechanism, various “tagging” or “labeling” methods have been developed. One such method is referred to as the arterial spin labeling (ASL) family of techniques. These techniques have been used to detect and provide a quantitative measure of neuronal activity. In conventional ASL, arterial blood is tagged by magnetic inversion or saturation proximal to a region-of-interest (ROI) being imaged. That is, ASL techniques tag blood some distance away from the slice or volume-of-interest to be imaged. The tagged blood flows into the ROI and the inflow is detected as a modulation of the longitudinal magnetization.
To create an image of flow, most ASL methods acquire one image with tagged blood and one with untagged (control) blood. These two images are subsequently subtracted to generate a perfusion image. Because of the inherently low signal of a single perfusion image, a series of perfusion images is typically averaged to generate a mean perfusion image with an increased signal-to-noise ratio (SNR).
Beyond CBF, there are a number of clinically useful parameters related to blood. One clinically relevant feature of blood is oxygen saturation (SaO2), from which oxygen extraction fraction (OEF) can be measured. Previous MR methods exist to measure OEF, but are limited. One class of methods attempts to measure the SaO2 based on the T2 (transverse) relaxation time of blood. The major challenge with such methods has been to separate the MR signal from various arterial, capillary, and venous compartments, whose blood will have different oxygen concentrations. For example, these methods are unable to cleanly target blood from post-capillary venules and cannot produce OEF maps on voxel-by-voxel basis. Instead, these methods have strict criteria for selecting voxels from which SaO2 (and subsequently OEF) is measured.
Another class of methods exploits susceptibility differences between vessels and their surrounding tissue to determine venous SaO2 (Yv). Susceptibility methods are particularly limited as they require manual, visual identification of draining veins, as identified by a functional activation experiment. These methods also require precise knowledge of vessel geometry and cannot be used to generate absolute Yv or OEF maps.
A “static dephasing”-based approach has also been explored to produce both OEF and CMRO2 maps. This approach, however, assumes random vessel orientation, no signal contributions from blood, and no diffusion effects. Moreover, the static-dephasing-regime theory used may not hold for capillaries.
Further still, there exists a multi-echo vascular occupancy (VASO) technique for estimating OEF. This technique requires prior estimates of baseline cerebral blood volume (CBV) and baseline Yv and, like other methods, only evaluates OEF for voxels activated during a neuroactivation task.
Thus, there have been several approaches proposed to measure SaO2 and OEF. However, these approaches are hindered by restrictive assumptions, are confounded by signal arising from tissue, and/or are unable to produce accurate OEF maps on a voxel-by-voxel basis.
It would, therefore, be desirable to have a system and method capable of accurately isolating signal from specific vascular compartments, measuring SaO2, and generating accurate OEF and CMRO2 maps on a voxel-by-voxel basis.